Computer vision systems generally include methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. One known problem in computer vision is determining whether image data contains specific objects, features, or activities. While humans may be able to quickly solve the aforementioned problem, the field of computer vision systems is still a developing understanding for the general case of arbitrary objects in arbitrary situations.
Robust computer vision algorithms are highly beneficial to Augmented Reality use cases. There may be a variety of algorithms and parameters to achieve a particular task. Many computer vision algorithms depend on choices for parameters or features and are dependent on specific tuning for a particular scenario. No set of universal parameters or algorithms may work for all scenarios and it is difficult to infer these parameters “on the fly” without additional information.
Accordingly, improved computer vision techniques are desirable.